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Review
. 2020 Sep;47(3):435-448.
doi: 10.1016/j.clp.2020.05.002. Epub 2020 May 20.

Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit

David Van Laere  1 , Marisse Meeus  2 , Charlie Beirnaert  3 , Victor Sonck  4 , Kris Laukens  3 , Ludo Mahieu  2 , Antonius Mulder  2
Affiliations
Review

Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit

David Van Laere et al. Clin Perinatol. 2020 Sep.

Abstract

Hemodynamic support in neonatal intensive care is directed at maintaining cardiovascular wellbeing. At present, monitoring of vital signs plays an essential role in augmenting care in a reactive manner. By applying machine learning techniques, a model can be trained to learn patterns in time series data, allowing the detection of adverse outcomes before they become clinically apparent. In this review we provide an overview of the different machine learning techniques that have been used to develop models in hemodynamic care for newborn infants. We focus on their potential benefits, research pitfalls, and challenges related to their implementation in clinical care.

Keywords: Hemodynamic support; Machine learning; Monitoring data; Predictive analytics; Preterm infants; Time series data.

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Conflict of interest statement

Disclosure D. Van Laere is the principal investigator of a machine learning research project (iNNOCENS project = Improving Neonatal Outcome by the Clinical implementation of an early notification system). T. Mulder and K. Laukens are in receipt of an Industrial Research fund granted by the University of Antwerp (IOF-POC 3882 iNNOCENS project). V. Sonck is employed by ML6 and is contracted as a third party for consultancy in machine learning in the iNNOCENS project.

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